Dual phase change coupled battery immersion cooling intelligent control method and system

Abstract

This invention discloses a dual-phase-change coupled intelligent control method and system for battery immersion cooling, relating to the field of intelligent battery cooling technology. The dual-phase-change coupled intelligent control method and system includes: S1, collecting and preprocessing temperature data and operational characteristic data to construct a standardized thermal state dataset; S2, evaluating the stability of the boiling state and dynamically adjusting the local channel liquid supply rhythm; S3, analyzing the melt state of the composite phase change material's melt coverage and driving changes in the auxiliary condenser's start-stop strategy; and S4, constructing a main-auxiliary dual-loop control structure to adjust the operating intensity of the main circulation pump and the bidirectional pump in real time. This solves the problem that existing liquid cooling systems have limited sensible heat capacity and uncontrolled boiling phase change processes when facing high-frequency, high-rate charge-discharge cycles, making it difficult to achieve precise peak-shaving control of heat in the core battery region.

Description

Technical Field

[0001] This invention relates to the field of intelligent battery cooling technology, specifically to an intelligent control method and system for battery immersion cooling with dual phase change coupling. Background Technology

[0002] With the increasing demand for high power density and long driving range in power batteries, immersion cooling has received widespread attention as a new generation of battery thermal management solutions, ensuring both safety and heat transfer efficiency. Existing immersion cooling technologies are mostly based on a single cooling medium, exchanging heat on the cell surface through liquid flow scouring, phase change evaporation, or natural convection. They combine a fixed flow path structure and a preset operating rhythm to carry out temperature control management, thereby achieving temperature control and uniform heat distribution during operation.

[0003] For example, the invention patent with announcement number CN118693402B provides an immersion liquid-cooled energy storage battery box and its temperature control optimization method, including: real-time monitoring of the temperature of each cell to obtain dynamic temperature data of the cells and generate a cell temperature change map; intelligent analysis based on the cell temperature change map to determine whether there are abnormal cell temperatures and identify cells with abnormal temperatures; determining the optimal liquid flow rate based on the temperature change of the cells with abnormal temperatures, and adjusting the flow rate of the coolant corresponding to the cells with abnormal temperatures based on the optimal liquid flow rate; and adaptively adjusting the flow rate of the coolant in the hollow flow channel corresponding to the cells with normal temperatures based on a time-flow rate control strategy combined with the current solar irradiation angle. This invention helps to reduce the temperature difference between the cells in the energy storage battery box, extend the cell life, achieve precise control of liquid cooling of the cells in the energy storage battery box, and control the liquid cooling energy consumption while ensuring that the cell temperature is maintained at a certain level, thereby reducing energy storage costs.

[0004] For example, the invention patent with announcement number CN115764080B discloses a battery thermal runaway intelligent control method and system; the method includes: acquiring the battery pack temperature rise rate; opening and closing the solenoid valve of the battery pack according to the battery pack temperature rise rate and based on a preset opening determination rule; if the battery pack temperature rise rate meets the opening determination rule, controlling the solenoid valve to open; sending a PWM signal to drive the electronic water pump to operate at a default speed so that the water-cooled unit injects antifreeze water into the battery pack; the embodiment of this application realizes the reduction of battery temperature. When the battery experiences the initial stage of thermal runaway, by injecting a large amount of coolant into the thermally runaway battery pack, the thermal runaway of the battery is controlled to continue to deteriorate, and the fire is extinguished in the initial stage.

[0005] However, these cooling methods generally suffer from high response rigidity, weak controllability, and poor adaptability to operating conditions. Especially under conditions such as high-rate charging and discharging, frequent start-stop cycles, or sudden hot spot outbreaks, it is difficult to coordinate local heat transfer imbalances and cooling resource allocation in a timely manner. Furthermore, existing immersion cooling systems typically lack mechanisms to discriminate cooling behavior under different thermal states, and lack dynamic perception and response logic for local temperature rise rates, bubble nucleus morphology evolution, and phase transition stability characteristics, making it difficult to achieve proactive reconfiguration of the cooling structure and rhythmic adjustment. In complex operating scenarios, a single working fluid heat transfer mechanism cannot simultaneously meet the rapid cooling requirements under high heat flux conditions and the local heat homogenization control target under steady-state conditions. This results in a lack of synergistic improvement in cooling efficiency and temperature control accuracy, limiting the system's dynamic adaptability to high heat loads and non-uniform temperature fields. Therefore, there is an urgent need to construct a smart control method and device for battery immersion cooling based on dual phase transition coupling to achieve multi-working fluid coordinated temperature regulation, dynamic response of local structures, and intelligent closed-loop control of the cooling process, thereby improving the overall adaptability and precision of thermal management.

[0006] To address the above issues, there is an urgent need for a smart control method and system for battery immersion cooling with dual phase change coupling. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a dual phase change coupled intelligent control method and system for battery immersion cooling, which solves the problem that existing liquid cooling systems have limited sensible heat capacity and runaway boiling phase change process when facing high-frequency, high-rate charging and discharging, making it difficult to achieve precise peak-shaving control of heat in the core area of ​​the battery.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a dual-phase-change coupled intelligent control method and system for battery immersion cooling, comprising: S1, collecting temperature data and operating characteristic data during battery operation, and preprocessing the collected temperature data and operating characteristic data to construct a standardized thermal state dataset; S2, based on the standardized thermal state dataset, performing a stability assessment of the boiling state, and dynamically adjusting the local channel liquid supply rhythm based on the stability assessment results; S3, based on the standardized thermal state dataset, performing a melt state analysis of the melt coverage rate of the composite phase change material in the PCM honeycomb panel, and driving the change of the auxiliary condenser start-stop strategy based on the melt state analysis results; S4, integrating the stability assessment results and the melt state analysis results to construct a main and auxiliary dual-loop control structure, and adjusting the operating intensity of the main circulation pump and the bidirectional pump in real time.

[0011] Further, the specific steps for collecting temperature data and operational characteristic data during battery operation are as follows: Collecting temperature data during battery operation, including: real-time temperature of the coolant, saturated boiling point temperature of the coolant under current pressure, local temperature difference of the coolant in the boiling zone, and induced point temperature of the PCM honeycomb panel surface; simultaneously calculating and recording the average temperature of the coolant in the current flow area and the highest temperature value among all measuring points; collecting operational characteristic data, including: return coolant flow rate in the condensation circuit, total current coolant circulation flow rate, and temperature of each thermocouple measuring point in the thermocouple array sensor; and recording the total number of thermocouple measuring points and the number of melting measuring points with real-time temperatures equal to or greater than the solid-liquid transition temperature of the material.

[0012] Furthermore, the specific steps for preprocessing the collected temperature data and operational characteristic data to construct a standardized thermal state dataset are as follows: The temperature data undergoes mean filtering and differential smoothing to suppress noise and spike interference; outlier removal and wavelet denoising are performed to obtain stable local temperature difference values ​​and induced point temperature values; boundary correction ensures the accuracy of the average and maximum temperature values. The operational characteristic data undergoes flow signal filtering and trend reconstruction to stabilize the reflux and overall circulation flow; spatial interpolation is used to reconstruct the temperature distribution of the thermocouple array sensor; statistical correction accurately obtains the number of melting measurement points. After completing the preprocessing of the temperature data and operational characteristic data, a unified normalization and dimensionless transformation is performed to construct a standardized thermal state dataset.

[0013] Furthermore, the specific steps for evaluating the stability of the boiling state based on the standardized thermal state dataset are as follows: subtract the saturated boiling point temperature of the coolant at the current pressure from the real-time temperature value of the coolant to obtain the instantaneous superheat of the coolant; take the absolute value of the instantaneous superheat of the coolant, add one, and take the logarithm to obtain the instantaneous temperature fluctuation value; add one to the local temperature difference value and take the logarithm to obtain the local thermal imbalance value; add one to the absolute value of the difference between the induced point temperature value and the average temperature value of the coolant, and take the logarithm to obtain the induced deviation value; multiply the instantaneous temperature fluctuation value, the local thermal imbalance value, and the induced deviation value to obtain the boiling stability identification value.

[0014] Furthermore, the specific steps for dynamically adjusting the local channel liquid supply rhythm based on the stability assessment results are as follows: Real-time comparison of the boiling stability identification value and boiling stability threshold of the current flow channel sub-section: When the boiling stability identification value is less than or equal to the boiling stability threshold, control the main circulation pump to maintain its current speed, keeping the main channel liquid flow intensity constant. Simultaneously, by controlling the intermittent liquid flow rhythm of the micro-groove array, maintain the balanced distribution of nucleation boiling excitation points, refresh the boiling stability identification value of the current sub-section in real time, and lock the pump speed adjustment level; When the boiling stability identification value is greater than the boiling stability threshold, immediately reduce the main circulation pump speed and simultaneously trigger the bidirectional pump to reverse low-speed pre-charge, allowing the subcooled coolant to be injected from the subcooled coolant storage tank into the corresponding tank; simultaneously, activate the switching path between the auxiliary condenser and the main condenser, suspend the liquid flow excitation of the original circulation path micro-groove induced structure, and switch to liquid supply to the internal flow channel of the PCM honeycomb panel top channel.

[0015] Furthermore, the specific steps for analyzing the melt state of the composite phase change material in the PCM honeycomb panel based on the standardized thermal state dataset are as follows: The difference between the highest temperature value and the average temperature value of the coolant at all measuring points, plus the temperature offset factor, is used as the numerator, and the highest temperature value is used as the denominator to obtain the temperature difference enhancement ratio; the number of melting measuring points is divided by the total number of thermocouple measuring points, and then the logarithm is added to obtain the melt response ratio; the temperature difference enhancement ratio is multiplied by the melt response ratio to obtain the PCM melt expansion identification value.

[0016] Furthermore, the specific steps for driving the auxiliary condenser start-up and shutdown strategy change based on the melt state analysis results are as follows: Real-time acquisition of the current PCM melt expansion identification value, and implementation of measures based on the trend of the PCM melt expansion identification value and the current structural state: When the PCM melt expansion identification value shows an upward trend compared to the PCM melt expansion identification value at the previous measuring point, immediately increase the bidirectional pump supply rate, switch the coolant inlet path to the bottom channel of the PCM honeycomb panel, open the auxiliary condenser liquid path, activate the evaporator-side heat exchange path, adjust the main circulation pump speed to the lowest value, and maintain the coolant flowing through the un-cooled section at a low speed. Melting zone; when the PCM melt expansion identification value does not change compared to the PCM melt expansion identification value at the previous measuring point, and the temperature in the main coolant channel remains unchanged, the current bidirectional pump liquid supply status and auxiliary condenser power remain unchanged, and the micro-groove flow channel at the bottom of the PCM honeycomb panel is opened to form a local uniform coolant disturbance; when the PCM melt expansion identification value shows a downward trend relative to the PCM melt expansion identification value at the previous measuring point, and the temperature sensor reports a decrease in coolant temperature, the bidirectional pump liquid injection is stopped, the auxiliary condenser operating load is reduced, and the main circulation pump is restored to the default speed, and the liquid flow path is reset to the default channel distribution.

[0017] Furthermore, based on the fusion stability assessment results and melt state analysis results, the specific steps for constructing the main and auxiliary dual-loop control structure are as follows: subtract the PCM melt expansion identification value from one and then multiply it by the boiling stability identification value to obtain the boil-melt comprehensive offset value; add one to the absolute value of the difference between the induction point temperature value and the average coolant temperature value and take the logarithm to obtain the induction deviation value; divide the return coolant flow rate in the condensation loop by the total coolant circulation flow rate and add one to obtain the condensation reflux compensation value; divide the boil-melt comprehensive offset value by the induction deviation value and then multiply it by the condensation reflux compensation value to obtain the joint pump control adjustment value.

[0018] Furthermore, the specific steps for real-time adjustment of the operating intensity of the main circulation pump and the bidirectional pump are as follows: Real-time comparison of the current combined pump control adjustment value with the pump control adjustment threshold, which includes a first pump control threshold and a second pump control threshold: When the combined pump control adjustment value is less than or equal to the second pump control threshold, the main circulation pump is set to maintain its current speed and the main channel liquid supply is locked, the bidirectional pump liquid injection path is closed, the existing operating conditions of the main condenser and auxiliary condenser are maintained, and the normal liquid flow of the microchannel is maintained; When the combined pump control adjustment value is greater than the second pump control threshold and less than or equal to the first pump control threshold, the main circulation pump is reduced. The pump speed is increased and the bidirectional pump is started to inject subcoolant at low speed. The return path is switched to the lower channel of the housing. At the same time, the heat exchange load of the main condenser is increased and the auxiliary condenser is kept running in conjunction. When the combined pump control adjustment value is greater than the first pump control threshold, the output of the main circulation pump is stopped and the liquid injection speed of the bidirectional pump is increased. The auxiliary condenser is started to run at full load and the heat exchange capacity of the main condenser is increased to the maximum operation. This ensures that the coolant maintains the maximum flow rate and heat exchange area utilization. The local activation of the V-shaped microgroove is suspended and the liquid level is adjusted to the height of the emergency channel to maintain the directional flow of coolant in the lower channel of the PCM honeycomb panel.

[0019] The second aspect of this invention provides a dual-phase-change coupled intelligent control system for battery immersion cooling, comprising: a multi-source data acquisition module for acquiring temperature data and operating characteristic data during battery operation, and preprocessing the acquired temperature data and operating characteristic data to construct a standardized thermal state dataset; a boiling zone stability identification module for evaluating the stability of the boiling state based on the standardized thermal state dataset, and dynamically adjusting the local channel liquid supply rhythm based on the stability evaluation results; a PCM melt state sensing module for analyzing the melt state of the composite phase change material in the PCM honeycomb panel based on the standardized thermal state dataset, and driving the change of the auxiliary condenser start-stop strategy based on the melt state analysis results; and an emergency condensation response module for integrating the stability evaluation results and the melt state analysis results to construct a main and auxiliary dual-loop control structure, and adjusting the operating intensity of the main circulation pump and the bidirectional pump in real time.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The dual phase change coupled battery immersion cooling intelligent control method and system, by introducing the synergistic coupling of coolant phase change and PCM solid-liquid phase change, achieves rapid suppression of transient temperature rise under high heat flux density conditions.

[0023] (2) The dual phase change coupled battery immersion cooling intelligent control method and system realizes intelligent allocation of coolant flow rate and return path by constructing a dual closed-loop control logic of main circulation pump and bidirectional pump.

[0024] (3) The dual phase change coupled battery immersion cooling intelligent control method and system realizes the active release of cooling redundancy capacity under emergency heat dissipation state through the linkage control of condensation reflux compensation and liquid circuit switching.

[0025] (4) The dual phase change coupled battery immersion cooling intelligent control method and system realizes dynamic judgment and real-time adjustment of cooling state through the joint calculation of boiling stability identification value and PCM melt expansion identification value.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a flowchart of the intelligent control method for battery immersion cooling with dual phase change coupling according to the present invention;

[0028] Figure 2 This is a structural diagram of the intelligent control system for battery immersion cooling with dual phase change coupling according to the present invention;

[0029] Figure 3 This is a line graph of the combined pump control adjustment value involved in this invention;

[0030] Figure 4 This is a diagram of the dual-loop collaborative logic involved in this invention;

[0031] Figure 5 This is a schematic diagram of the dual phase change cooling involved in this invention;

[0032] Figure 6 This is a PCM diagram of the honeycomb panel involved in this invention;

[0033] Figure 7 This is a diagram of the V-shaped microgroove flow channel involved in this invention.

[0034] In the diagram, 1. Subcooled coolant reservoir; 2. Bidirectional pump; 3. Pressure and temperature sensors; 4. Housing; 5. Temperature sensor; 6. Thermocouple array sensor; 7. Energy storage battery; 8. PCM honeycomb panel; 9. Main circulation pump; 10. Auxiliary condenser; 11. Main condenser; 12. Liquid level; 13. Aluminum alloy plate; 14. Paraffin and graphite composite phase change material; 15. V-groove. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figures 1-7 This invention provides a technical solution: a dual-phase change coupled battery immersion cooling intelligent control method and system, including S1, collecting temperature data and operating characteristic data during battery operation, and preprocessing the collected temperature data and operating characteristic data to construct a standardized thermal state dataset; S2, based on the standardized thermal state dataset, performing a stability assessment of the boiling state, and dynamically adjusting the local channel liquid supply rhythm based on the stability assessment results; S3, based on the standardized thermal state dataset, performing a melt state analysis on the melt coverage of the composite phase change material in the PCM honeycomb panel 8, and driving the change of the start-stop strategy of the auxiliary condenser (10) based on the melt state analysis results; S4, integrating the stability assessment results and the melt state analysis results to construct a main and auxiliary dual-loop control structure, and adjusting the operating intensity of the main circulation pump 9 and the bidirectional pump 2 in real time.

[0037] Specifically, the steps for collecting temperature data and operational characteristic data during battery operation are as follows:

[0038] Temperature data is collected during battery operation, including: real-time coolant temperature, obtained through continuous sampling by temperature sensors distributed in the main circulation channel and on the outer wall of the battery module; saturated boiling point temperature of the coolant under the current pressure, dynamically calculated by combining real-time monitoring results from pressure sensors with the physical properties of the cooling medium; local temperature difference of the coolant in the boiling zone, determined by comparing the temperature differences between upstream and downstream and lateral branches to determine the degree of local heat accumulation; induced point temperature value on the surface of PCM honeycomb panel 8, collected in real-time by highly sensitive thermocouples deployed on the microgroove induced surface, reflecting the local thermal state of the bubble nucleation region; and simultaneously, the average coolant temperature value in the current flow area is calculated and recorded to characterize the overall fluid temperature field distribution, as well as the highest temperature value among all measuring points, used to identify potential hot spots and abnormal heating trends.

[0039] The system collects operational characteristic data, including: the flow rate of the return coolant in the condensation circuit, which is measured in real time by a flow meter installed in the return pipeline to reflect the condensation efficiency; the current total flow rate of the coolant circulation, which is obtained by superimposing the output flow rates of the main circulation pump and the bidirectional pump; the temperature of each thermocouple measuring point in the thermocouple array sensor 6, which is used to construct a multi-point temperature distribution field and identify local phase transition states; and records the total number of thermocouple measuring points to ensure the integrity of spatial sampling. At the same time, it counts the number of melting measuring points whose real-time temperature is equal to or greater than the solid-liquid transition temperature of the material, which is used to dynamically calculate the PCM melt expansion identification value and reflect the remaining level of phase transition buffering capacity.

[0040] In this implementation scheme, by simultaneously collecting and calculating temperature data and operational characteristic data during battery operation, a complete set of thermal state parameters is constructed, providing a basic input for subsequent boiling stability identification and PCM melt expansion identification. The temperature data section can characterize the overall thermal state and local differences of the coolant, ensuring accurate understanding of real-time superheat, hot spot distribution, and induced temperature shifts; the operational characteristic data section can reflect the reflux efficiency of the condensation loop, the uniformity of circulation flow, and the actual melting coverage of the phase change material, thereby achieving multi-dimensional quantification of heat conduction, phase change endothermic reaction, and condensation reflux.

[0041] Specifically, the preprocessing of collected temperature and operational characteristic data to construct a standardized thermal state dataset involves the following steps: Temperature data undergoes mean filtering and differential smoothing to suppress noise and spikes caused by electromagnetic interference, sensor jitter, and transient changes during continuous sampling. Simultaneously, outlier removal and wavelet denoising are performed to eliminate outliers that deviate excessively from the mean in the sampling sequence. Stable local temperature differences and induced temperature values ​​at the 8-surface point of the PCM honeycomb panel are extracted. Boundary correction is used to adjust the average and maximum coolant temperatures to ensure the accuracy and stability of the overall temperature field characterization. Operational characteristic data undergoes flow signal filtering and trend reconstruction to avoid interference caused by cooling... The transient fluctuations caused by the switching of the condensate circuit and the main circulation pump are mitigated to ensure a smooth and continuous curve of the total value of the return coolant flow rate and the total coolant circulation flow rate. Simultaneously, spatial interpolation is used to continuously reconstruct the multi-point temperature data from the thermocouple array sensor 6, obtaining a complete temperature distribution field within the battery cooling area. Statistical correction methods are used to revise the number of melting measurement points, ensuring an accurate proportional relationship between the melting points and the total number of measurement points. After multi-dimensional preprocessing of temperature and operational characteristic data, uniform normalization and dimensionless transformation are performed to convert different physical quantities into comparable standard parameters. Finally, a standardized thermal state dataset is constructed, providing reliable input for boiling stability identification and PCM melt propagation identification.

[0042] In this implementation scheme, multi-level preprocessing of temperature and operational characteristic data suppresses and corrects noise interference, abnormal deviations, and uneven distributions in the original acquired signals, thereby ensuring the integrity and accuracy of the input data. After filtering, smoothing, denoising, and boundary correction, the temperature data accurately reflects the true changes in the average state of the coolant, local temperature differences, and induced temperature points, avoiding misleading subsequent judgments due to instantaneous changes and sensing errors. After flow reconstruction, spatial interpolation, and statistical correction, the operational characteristic data provides a continuous description of the thermocouple array temperature field while ensuring the stability of the return flow and overall circulation flow, and accurately measures the number of melting measurement points. Through unified normalization and dimensionless transformation, the final standardized thermal state dataset provides reliable input support for the core calculation modules of boiling stability identification and PCM melt expansion identification, ensuring the real-time performance and robustness of the control logic under dynamic operating conditions.

[0043] Specifically, based on a standardized thermal state dataset, the stability assessment of the boiling state involves the following steps: subtracting the saturated boiling point temperature of the coolant at the current pressure from the real-time temperature value of the coolant to obtain the instantaneous superheat of the coolant; taking the absolute value of the instantaneous superheat of the coolant, adding one, and then taking the logarithm to obtain the instantaneous temperature fluctuation value; adding one to the local temperature difference value and then taking the logarithm to obtain the local thermal imbalance value; adding one to the absolute value of the difference between the induced point temperature value and the average temperature value of the coolant, and then taking the logarithm to obtain the induced deviation value; and multiplying the instantaneous temperature fluctuation value, the local thermal imbalance value, and the induced deviation value to obtain the boiling stability identification value.

[0044] The formula for calculating the boiling stability identification value is:

[0045] ;

[0046] In the formula: This represents the instantaneous superheat of the coolant, used to measure the deviation of the current coolant temperature from its saturation boiling point. It is an important basis for determining whether boiling has been established and whether it is in a nucleated state. Its calculation method is as follows: = - ,in: It represents the real-time temperature value of the coolant, which reflects the thermal state of the cooling medium around the battery module. It is derived from liquid temperature sensors installed in the flow channels and inside the housing. This represents the saturation boiling point temperature of the coolant under the current pressure, and is used as a reference point for boiling criteria. It is derived from the calculation results of the pressure sensor combined with the coolant boiling point function. It represents the local temperature difference of the coolant in the boiling zone, which is used to quantify the degree of local heat accumulation and non-uniform heat transfer. It is an important supplementary parameter for judging whether there is instability in boiling. It is calculated by the temperature difference collected by temperature sensors placed at different locations in the boiling zone, and reflects whether the heat disturbance is released uniformly. This value represents the induction point temperature on the surface of the PCM honeycomb panel, reflecting the actual thermal state at the location where bubble nucleation occurs. It is a key indicator for determining whether bubbles can successfully form and detach. This value originates from a highly sensitive thermocouple measuring point attached to the center of the V-shaped microgroove of the honeycomb panel. This represents the average coolant temperature within the current flow region, serving as a reference for the induction point temperature and reflecting the overall thermal state of the boiling zone. This parameter is derived from the average real-time data of multiple distributed temperature acquisition points within the same region.

[0047] In this implementation plan, the boiling state of the coolant in the controlled flow path is quantified to determine whether there is a risk of non-uniform boiling caused by instantaneous heat disturbance, local heat transfer distortion and heterogeneous heat conduction path, so as to provide a basis for judgment for dynamically adjusting the phase change cooling rhythm and path switching strategy.

[0048] Specifically, the steps for dynamically adjusting the local channel fluid supply rhythm based on stability assessment results are as follows:

[0049] The boiling stability identification value and boiling stability threshold of the current flow channel sub-section are compared in real time to dynamically determine whether the sub-section is still in the nucleate boiling stability range.

[0050] When the boiling stability identification value is less than or equal to the boiling stability threshold, the main circulation pump 9 is controlled to maintain its current operating speed to ensure that the liquid flow intensity in the main channel remains unchanged, thereby maintaining uniform coverage of the coolant in the heat dissipation area. At the same time, by controlling the intermittent liquid flow rhythm of the micro-groove array deployed on the surface of the PCM honeycomb plate 8, the local nucleation boiling excitation points are kept spatially balanced, avoiding the local film formation trend caused by flow deviation and single-point overheating. During this process, the boiling stability identification value of the current sub-section is refreshed in real time, and this value is used as the basis for locking the pump speed adjustment level, providing continuous monitoring support for subsequent regulation.

[0051] When the boiling stability identification value exceeds the boiling stability threshold, the speed of the main circulation pump 9 is immediately reduced to decrease the heat input intensity of the main circulation liquid flow to the high-temperature zone. Simultaneously, the bidirectional pump 2 is triggered to perform reverse low-speed pre-charge, allowing the subcooled coolant to be injected from the subcooled coolant storage tank 1 into the corresponding tank 4, forming a low-temperature compensation liquid flow to quickly alleviate the local overheating trend. At the same time, the switching path between the auxiliary condenser 10 and the main condenser 11 is activated to enhance heat exchange capacity and share the condensation load of the main circuit. During this process, the liquid flow excitation of the micro-groove induced structure in the original circulation path is suspended to avoid excessive accumulation of high-temperature bubbles. The liquid flow path is then switched to the internal diversion channel of the top channel of the PCM honeycomb panel 8 for liquid supply. By introducing directional cold liquid to form a condensation cover in the top area, the suppression effect on the risk of local film boiling is improved.

[0052] In this implementation scheme, by comparing the boiling stability identification value with the boiling stability threshold in real time, it dynamically determines whether the flow channel sub-section is within the safe range of nucleus boiling. Based on the determination result, corresponding liquid supply and condensation control measures are triggered to maintain the stability of the cooling process under different thermal states. When the identification value is below the threshold, the main circulation pump maintains normal liquid supply, and the intermittent liquid flow rhythm of the microchannel array ensures uniform distribution of boiling nuclei and avoids fluctuations in coolant supply. When the identification value exceeds the threshold, the local overheating is quickly weakened by reducing the flow rate of the main circulation pump, introducing subcoolant for pre-charge, and switching the condensation path, forming emergency cooling redundancy to prevent the occurrence of film boiling and the risk of drying out. Overall, this step realizes dynamic monitoring and differentiated response of the stability state of the boiling zone, ensuring the continuous controllability and safety of thermal management under high load conditions.

[0053] Specifically, based on the standardized thermal state dataset, the melting state analysis of the melt coverage of the composite phase change material in the PCM honeycomb panel (8) is performed as follows: the difference between the highest temperature value and the average temperature value of the coolant in all measuring points plus the temperature offset factor is used as the numerator, and the highest temperature value is used as the denominator to obtain the temperature difference enhancement ratio; the number of melting measuring points is divided by the total number of thermocouple measuring points and then logarithm is added to obtain the melting response ratio; the temperature difference enhancement ratio is multiplied by the melting response ratio to obtain the PCM melt expansion identification value.

[0054] The formula for calculating the PCM melt spread identification value is:

[0055] ;

[0056] This represents the highest temperature value collected from all measuring points. It is used to characterize the extreme response in the PCM thermal field and is an important reference for judging whether the upper phase change has been widely unfolded. It comes from the temperature measurement data of the top and edge heat concentration areas in the thermocouple array sensor 6. This represents the average coolant temperature within the current flow region, serving as a reference for the induction point temperature and reflecting the overall thermal state of the boiling zone. This parameter is derived from the average real-time data of multiple distributed temperature acquisition points within the same region. This indicates the number of melting measurement points whose real-time temperature is equal to or exceeds the solid-liquid transition temperature of the material. It is used to quantify the number of spatial nodes that have entered the melting state and is a basic indicator for deriving the melting coverage ratio. It is derived from the comparison logic between the sampling results of the thermocouple array sensor and the threshold. This represents the total number of thermocouple measuring points in the thermocouple array sensor, which is used as a normalization benchmark for the melt coverage rate. It is the denominator for determining the overall degree of melting and is derived from the total number of measuring points fixedly arranged on the PCM honeycomb panel 8. The temperature offset factor, ranging from 0 to 1.5, characterizes the absolute deviation of the highest temperature value at the current monitoring point from the cooling reference temperature. It is a key indicator for determining whether a local heat accumulation area has formed a thermal imbalance trend. Its value is derived from the deviation between the extreme temperature values ​​at different monitoring points and the average cooling temperature. In the specific calculation, the instantaneous temperature peak value is first extracted from each monitoring point and combined with the average temperature of the coolant in the current flow area to calculate the maximum temperature difference value as the initial offset intensity. Then, the spatial expansion of this temperature difference within the regional grid is evaluated, and the degree of aggregation of high temperature values ​​in the multi-point distribution is combined to form an offset density index. Finally, the temperature difference intensity and aggregation density are normalized and fused to construct the temperature offset factor. When the local temperature anomaly is much higher than the cooling reference and the duration is significantly prolonged, the temperature offset factor value is increased to enhance the coverage tilt of the phase change response to the local high heat risk. Conversely, if the temperature distribution is uniform and there are no sudden high temperatures, the temperature offset factor is reduced to avoid over-activation of the PCM expansion mechanism, thereby improving the adaptive accuracy and response efficiency of the cooling rhythm regulation.

[0057] This implementation scheme comprehensively assesses whether there is a significant coating melting trend in the current phase change thermal field. Its core function is to dynamically identify whether the response boundary of the PCM honeycomb structure has been breached during the heat accumulation process, and whether the local coating has entered the unsteady-state expansion region. The temperature difference ratio term in the formula reflects the degree of temperature deviation between the high-temperature point and the average cooling state, which can reveal the severity of local heat accumulation and cooling imbalance. The introduction of the temperature offset factor enhances the sensitivity to abnormal temperature rise at local abrupt change points. The ratio of the number of active points to the total number of monitoring points in the logarithmic term is used to capture the spatial spread rate of the melting trend. Through the above multi-factor coupling, this formula can not only quantitatively identify whether there is a melting risk, but also reflect the imbalance between the coating activity level and the lag in heat dissipation response, thereby providing a key criterion for triggering thermal field control strategies and improving the coating response identification accuracy and dynamic control capability under multi-point distributed temperature zones.

[0058] Specifically, the steps for driving the start-stop strategy change of the auxiliary condenser 10 based on the melt state analysis results are as follows: real-time acquisition of the current PCM melt expansion identification value, and implementation of differentiated control measures based on the numerical change trend of the PCM melt expansion identification value and the current structural state, in order to ensure the stable connection between the phase change buffering capacity and the coolant supply process.

[0059] When the PCM melt expansion identification value shows an upward trend compared to the previous measuring point, it is determined that the melting range of the PCM area is expanding. At this time, the liquid supply rate of the bidirectional pump 2 is immediately increased, but the increase is limited to within 80% of the upper limit of the flow rate to prevent excessive local flow velocity from causing liquid surface disturbance and imbalance. At the same time, the coolant inlet path is switched to the bottom channel of the PCM honeycomb panel 8, and the liquid path of the auxiliary condenser 10 is opened to activate the heat exchange path on the evaporation side. The speed of the main circulation pump 9 is reduced to the lowest setting, so that the coolant flows through the unmelted area at a low speed to achieve stable cold liquid coverage. This switching process has a 2-second delay to ensure the synchronization of flow path switching and condenser loading.

[0060] When the PCM melt expansion identification value remains unchanged compared to the previous measurement point, and the temperature curve in the main coolant channel shows no significant fluctuation, the steady-state maintenance phase begins. At this time, the liquid supply rate of the bidirectional pump 2 is locked in the current range, not exceeding 50% of the default flow rate, to maintain stable liquid injection. The power of the auxiliary condenser 10 remains unchanged, and the operating load is maintained below 70% to avoid increasing ineffective energy consumption. At the same time, the micro-groove flow channel at the bottom of the PCM honeycomb panel 8 is opened, and a local cold liquid coverage is formed through low-velocity uniform disturbance to prevent concentrated heat accumulation. This action has a 5-second response delay to confirm that the state is indeed stable before maintaining the liquid supply strategy.

[0061] When the PCM melt expansion identification value shows a downward trend and the temperature sensor 5 reports a synchronous decrease in coolant temperature, it is determined that the melting trend has eased. At this time, the injection of liquid by the bidirectional pump 2 is immediately stopped to prevent excessive flow caused by redundant coolant injection. The operating load of the auxiliary condenser 10 is simultaneously reduced to less than 30% of the rated power to save energy and maintain necessary condensation support. The main circulation pump 9 is gradually restored to the default speed to restore the normal distribution of liquid flow in the main channel. This process has a 3-second delay to avoid excessive flow field fluctuations caused by instantaneous load reduction and to ensure a smooth transition back to the steady-state operating path.

[0062] In this implementation scheme, by monitoring the numerical change trend of the PCM melt expansion identification value in real time, the evolution state of the phase change buffering capacity is dynamically determined, and a differentiated coolant supply and condensation control strategy is executed accordingly. This ensures temperature stability and cooling efficiency at different melting stages, and ensures the effective performance of the PCM buffering function under dynamic operating conditions. It achieves closed-loop regulation from acceleration suppression, stability maintenance to energy saving and decline.

[0063] Specifically, the steps for constructing a main-auxiliary dual-loop control structure by integrating the stability assessment results and the melt state analysis results are as follows: Subtract the PCM melt expansion identification value from one and then multiply it by the boiling stability identification value to obtain the boil-melt comprehensive offset value; Add one to the absolute value of the difference between the induction point temperature value and the average coolant temperature value and take the logarithm to obtain the induction deviation value; Divide the return coolant flow rate in the condensation loop by the total coolant circulation flow rate and add one to obtain the condensation reflux compensation value; Divide the boil-melt comprehensive offset value by the induction deviation value and then multiply it by the condensation reflux compensation value to obtain the joint pump control regulation value.

[0064] The formula for calculating the combined pump control regulation value is:

[0065] ;

[0066] In the formula: The boiling stability identification value is used to quantify whether the current boiling state of the coolant in the controlled flow channel is stable. It is a thermal stability index for determining whether the main circuit should continue to be driven. It is derived from the fusion calculation of multi-point temperature difference, superheat and induced temperature difference. The PCM melt expansion identification value is used to quantify the melting ratio of composite phase change material in PCM honeycomb panels. It is a structural indicator for judging whether the buffer capacity is about to be exhausted, and it is derived from the discrimination statistics of temperature in each zone by thermocouple array sensors. This represents the surface induction point temperature of the PCM honeycomb panel, reflecting the actual thermal state at the location where bubble nucleation occurs. It is a key indicator for determining whether bubbles can successfully form and detach. This value originates from a highly sensitive thermocouple measuring point attached to the center of the V-shaped microgroove of the honeycomb panel. This represents the average coolant temperature within the current flow region, serving as a reference for the induction point temperature and reflecting the overall thermal state of the boiling zone. This parameter is derived from the average real-time data of multiple distributed temperature acquisition points within the same region. It represents the flow rate of the return coolant in the condensation circuit, used to quantify the recovery capability of the current condensation path, and is derived from the flow rate monitoring value of the condensate return measurement point; This represents the total current coolant circulation flow rate, used as a normalized reference for the condensate recirculation ratio, and is derived from the sum of the flow rates of the main circulation pump and the bidirectional pump.

[0067] In this implementation example, the boiling stability identification value of Example 1 is set to 1.5, the PCM melt expansion identification value is set to 0.20, the surface induction point temperature is 46.5°C, the average coolant temperature is 45.0°C, the return coolant flow rate is 0.6, and the total coolant circulation flow rate is 2.4. In Example 2, the boiling stability identification value is set to 1.8, the PCM melt expansion identification value is set to 0.35, the surface induction point temperature is 47.0°C, the average coolant temperature is 45.2°C, the return coolant flow rate is 0.75, and the total coolant circulation flow rate is 2.5. In Example 3, the boiling stability identification value is set to 1.6, the PCM melt expansion identification value is set to 0.40, the surface induction point temperature is 46.0°C, the average coolant temperature is 44.8°C, the return coolant flow rate is 0.7, and the total coolant circulation flow rate is 2.3. In Example 4, the boiling stability identification value is set to 2.0, the PCM melt expansion identification value is set to 0.50, the surface induction point temperature is 46.5°C, the average coolant temperature is 45.2°C, the return coolant flow rate is 0.7, and the total coolant circulation flow rate is 2.3. The temperature value was 48.0, the average coolant temperature was 46.5, the return coolant flow rate was 0.9, and the total coolant circulation flow rate was 2.7. For Example 5, the boiling stability identification value was set to 1.4, the PCM melt expansion identification value was set to 0.25, the surface induction point temperature was 45.8, the average coolant temperature was 44.9, the return coolant flow rate was 0.55, and the total coolant circulation flow rate was 2.2. For Example 6, the boiling stability identification value was set to 1.9, the PCM melt expansion identification value was set to 0.3, the surface induction point temperature was 47.5, the average coolant temperature was 46.2, the return coolant flow rate was 0.85, and the total coolant circulation flow rate was 2.6. For Example 7, the boiling stability identification value was set to 1.7, the PCM melt expansion identification value was set to 0.45, the surface induction point temperature was 46.8, the average coolant temperature was 45.6, the return coolant flow rate was 0.65, and the total coolant circulation flow rate was 2.1. The combined pump control adjustment value for each instance is calculated, as shown in Table 1, which contains the combined pump control adjustment value data.

[0068] Table 1. Combined Pump Control Adjustment Value Data Table

[0069]

[0070] like Figure 3 As shown, this is a line graph of the combined pump control adjustment value provided in the example of this application. (See Table 1 and...) Figure 3It can be seen that Example 6 has the highest combined pump control regulation value, indicating that under the conditions of high boiling stability identification value, moderate PCM melt expansion identification value, and coordinated flow distribution ratio, it has a strong overall thermal state control capability, demonstrating adaptability to complex operating conditions and redundant cooling capability. It primarily reflects its high sensitivity and rapid response characteristics in pump speed regulation and condensation reflux compensation. Example 4 has the lowest combined pump control regulation value. Although it has the highest boiling stability identification value, the high PCM melt expansion identification value and relatively uneven flow distribution ratio lead to a decrease in comprehensive control capability, resulting in insufficient dynamic adaptability. It automatically reduces its priority in thermal management optimization strategies and is retained more as a redundant monitoring indicator to avoid excessive resource investment and redundant intervention. The line graph of combined pump control regulation value intuitively reflects the distribution characteristics of thermal management regulation capability of each example under different operating conditions. The higher the value, the stronger the sensitivity and control value of the example to complex operating conditions, and the more likely it is to be prioritized for pump speed adjustment and condensation strategy optimization.

[0071] Specifically, the steps for real-time adjustment of the operating intensity of the main circulation pump 9 and the bidirectional pump 2 are as follows:

[0072] The current joint pump control adjustment value is compared with the pump control adjustment threshold in real time. The pump control adjustment threshold consists of a first pump control threshold and a second pump control threshold, which are used to distinguish the pump speed switching and condensation path adjustment logic under different cooling conditions.

[0073] When the combined pump control adjustment value is less than or equal to the second pump control threshold, it is determined that the overall thermal state is still in the stable range. At this time, the main circulation pump 9 is set to maintain the current speed and lock the main channel liquid supply to ensure that the liquid flow stably covers the core heat dissipation area. At the same time, the liquid injection passage of the bidirectional pump 2 is closed to avoid ineffective cold liquid consumption. The main condenser 11 and the auxiliary condenser 10 maintain the existing operating conditions, maintain the basic heat dissipation rhythm of the heat exchange channel, and keep the micro-groove flow channel arranged on the surface of the PCM honeycomb panel 8 in normal liquid flow to ensure the continuous activity of the local nucleation boiling induction point.

[0074] When the combined pump control adjustment value is greater than the second pump control threshold and less than or equal to the first pump control threshold, it enters a medium adjustment state. At this time, the speed of the main circulation pump 9 is immediately reduced to decrease the heat input intensity of the main circuit, and the bidirectional pump 2 is simultaneously turned on to inject subcooled liquid at a low speed, so that the compensation coolant flows into the lower channel of the tank 4 through the switched return liquid path to cool the PCM area that has not yet completely melted from the bottom. At the same time, the heat exchange load of the main condenser 11 is increased to enhance the condensation capacity, and the auxiliary condenser 10 is kept in the linked open state to achieve dual redundant heat dissipation of the condensation path.

[0075] When the combined pump control adjustment value exceeds the first pump control threshold, an extreme emergency state is declared. At this time, the output of the main circulation pump 9 is immediately stopped, and the injection speed of the bidirectional pump 2 is increased, allowing a large amount of subcooled liquid to be rapidly injected into the cooling channel. The auxiliary condenser 10 is activated to full-load operation, and the heat exchange capacity of the main condenser 11 is increased to the maximum operating level to ensure that the coolant maintains maximum flow rate and maximum heat exchange area utilization, rapidly reducing the overall heat load. At the same time, the local liquid flow activation of the V-shaped microgroove is suspended to prevent the boiling instability phenomenon in the high-temperature zone from being further amplified, and the liquid level 12 is adjusted to the height of the emergency channel, so that the coolant maintains directional flow in the lower channel of the PCM honeycomb panel 8, ensuring that the phase change material obtains maximum coolant coverage in a short time.

[0076] like Figure 4 The diagram shown illustrates the dual-loop collaborative logic provided in this application, demonstrating the dynamic adjustment approach in the coupled heat dissipation process of phase change material (PCM) and coolant. First, temperature and pressure information during operation is collected by a thermocouple array sensor, pressure and temperature sensors, and a PCM temperature module monitoring device. This sensor data is then processed by a calculation module to obtain the instantaneous superheat of the coolant and the melting point of the PCM. Next, a judgment phase is initiated: when the monitoring results show that the PCM melting rate is greater than 90% and the coolant superheat exceeds 5°C, an emergency response protocol is triggered, indicating that the current buffer capacity is about to be exhausted and an emergency cooling phase is required. If this condition is not met, the conventional PID controller phase is entered, performing closed-loop adjustments to the pump speed and flow rate based on the real-time status. Finally, the pump speed adjustment unit dynamically optimizes the coolant circulation flow rate and local injection strategy. The dual-loop collaborative logic diagram represents a logical chain from sensor acquisition to calculated indicators, condition judgment, closed-loop control, and execution adjustment, ensuring the stability of the battery cooling process under high-power operation and promptly switching to a redundant heat dissipation mechanism in extreme conditions.

[0077] like Figure 5The diagram illustrates the dual phase change cooling provided in this application. On the left is a subcooled coolant storage tank 1, supplied with coolant via a bidirectional pump 2. Pressure and temperature sensors 3 are also present to monitor the flow pressure and temperature of the liquid in real time. After entering the housing 4, the coolant immerses and cools the internal energy storage battery 7. Temperature sensors 5 and thermocouple array sensors 6 are distributed inside the housing, forming a dual phase change coupled heat dissipation structure in conjunction with the internal PCM honeycomb panel 8. The PCM honeycomb panel utilizes both the phase change evaporation of the coolant for heat absorption and the solid-liquid phase change of the PCM material to buffer peak heat flux, improving overall heat dissipation stability. The coolant is driven by the main circulation ring pump 9 into the condensation unit on the right, including an auxiliary condenser 10 and a main condenser 11. In the condenser, heat is effectively released and the liquid condenses before returning to the storage tank 1, forming a closed loop. The liquid level 12 marked in the diagram indicates the height of the coolant during operation, ensuring a stable cooling path.

[0078] like Figure 6 The diagram shown is a honeycomb PCM (Polymerized Phase Change Module) provided in this application, illustrating a core component used in a battery immersion cooling system for achieving phase change energy storage and thermal buffering. As can be seen in the diagram, the honeycomb cell is constructed from an aluminum alloy plate 13 forming a regular hexagonal honeycomb structure with excellent thermal conductivity and mechanical strength, enabling rapid heat transfer from the battery to the phase change region. The honeycomb cell is filled with a paraffin-graphite composite phase change material 14. This material combines the solid-liquid phase change heat absorption characteristics of paraffin with the high thermal conductivity of graphite, allowing it to absorb and store a large amount of heat during high-rate battery operation, while simultaneously accelerating heat diffusion and preventing localized overheating. This honeycomb PCM design creates a dual phase change coupling between the coolant and the PCM composite material: on one hand, the coolant absorbs sensible and latent heat during boiling; on the other hand, the PCM absorbs and gently releases heat through solid-liquid transition, thereby achieving efficient thermal buffering and temperature control stability during battery operation. The honeycomb geometry not only improves heat dissipation efficiency but also facilitates uniform distribution of liquid flow between cells, avoiding dead zones and localized hot spots. The PCM diagram of the honeycomb panel emphasizes the design concept of aluminum alloy honeycomb frame filled with composite phase change material, which is a key component to achieve the dual phase change coupled cooling effect.

[0079] like Figure 7The diagram shows a V-shaped microgroove flow channel provided in this application, illustrating a key cooling unit used to enhance heat transfer and bubble removal in battery immersion cooling. In the diagram, a flow channel with regular grooves is arranged between the energy storage battery 7 and the PCM honeycomb panel 8, with multiple V-shaped grooves 15 machined inside the channel. This V-shaped microgroove structure guides the coolant to form micro-scale disturbances at the battery-PCM interface during liquid flow, increasing the contact area between the fluid and solid interfaces and effectively enhancing bubble nucleation and timely detachment during nucleation boiling, avoiding localized heat transfer deterioration caused by bubble aggregation. Simultaneously, the V-shaped groove geometry generates a secondary vortex effect in the coolant during flow, thereby increasing the coolant renewal frequency and promoting rapid heat dissipation from localized high-temperature areas. Compared to smooth flow channels, V-shaped microgrooves have higher heat transfer coefficients and stability, significantly reducing the risk of film boiling under high heat flux density conditions.

[0080] In this implementation plan, by comparing the joint pump control adjustment value with the graded threshold in real time, the operating state is divided into three scenarios: normal steady state, overheat warning, and extreme emergency. Based on this, differentiated pump speed control and condenser path adjustment strategies are triggered. When the adjustment value is low, the main circulation pump and condenser maintain normal operation to ensure the stability of coolant supply. When the adjustment value enters the middle range, the overheating trend is mitigated by reducing the main circulation pump speed, starting the bidirectional pump for low-speed liquid injection, and increasing the condenser load. When the adjustment value exceeds the high threshold, emergency measures are implemented, including shutting down the main circulation pump, high-speed liquid injection by the bidirectional pump, full-load operation of the condenser, and liquid level adjustment, to provide maximum cooling capacity under extreme conditions. Overall, this step achieves graded response of thermal management under different operating conditions, ensuring a dynamic balance between cooling efficiency and safety.

[0081] The second aspect of this invention provides a dual-phase-change coupled intelligent control system for battery immersion cooling, comprising: a multi-source data acquisition module for acquiring temperature data and operational characteristic data during battery operation, wherein the temperature data includes the real-time temperature of the coolant, the saturation boiling point temperature under the current pressure, the local temperature difference in the boiling zone, and the induced temperature at the surface of the PCM honeycomb panel 8, and simultaneously calculates and records the average temperature of the coolant and the highest temperature value among all measuring points; the operational characteristic data includes the reflux coolant flow rate of the condensation circuit, the current total coolant circulation flow rate, and the real-time temperature of each measuring point in the thermocouple array sensor 6, and records the total number of measuring points and the number of melting measuring points. After performing mean filtering, differential smoothing, wavelet denoising, and boundary correction on the acquired temperature data and operational characteristic data, a unified normalized dimensionless transformation is performed to finally construct a standardized thermal state dataset, providing reliable input for subsequent modules.

[0082] The boiling zone stability identification module is used to perform a fusion analysis on the superheat of the coolant, the local temperature difference distribution, and the temperature deviation of the induced point based on a standardized thermal state dataset, and calculate the boiling stability identification value, thereby realizing the dynamic determination of whether the boiling zone is in the nucleus boiling state; and based on the stability assessment results, it adjusts the liquid supply rhythm of the local channel in real time, and ensures the spatially balanced distribution of boiling induction points by controlling the intermittent liquid flow of the microchannel and switching the flow path, thus avoiding the occurrence of local film boiling.

[0083] The PCM melt state sensing module is used to accurately identify the melt coverage of the composite phase change material in the PCM honeycomb panel 8 based on a standardized thermal state dataset, calculate the melt expansion identification value, and analyze whether the phase change buffer capacity is about to be exhausted based on its changing trend. When the melt rate is close to the limit, the module drives the start-stop strategy of the auxiliary condenser 10 to dynamically switch, forming redundancy compensation of the condensation circuit to ensure the continuous function of the phase change buffer under high heat load conditions.

[0084] The emergency condensation response module integrates the boiling zone stability assessment results and the PCM melt state analysis results to construct a dual-loop control structure for the main circulation and auxiliary condensation. When in steady state, it maintains the normal operating conditions of the main circulation pump 9 and the bidirectional pump 2. When boiling is unstable and the melt coverage increases rapidly, it promptly increases the injection rate of the bidirectional pump and starts the auxiliary condenser 10 to reduce the operating intensity of the main circulation pump 9, thereby maximizing the flow and heat exchange capacity of the coolant and rapidly mitigating the risk of thermal imbalance under extreme conditions.

[0085] In this implementation plan, the multi-source data acquisition module comprehensively acquires temperature information and fluid operation characteristics during battery operation. After filtering, smoothing, denoising and normalization, a unified and standardized thermal state dataset is constructed to provide accurate and stable input for subsequent analysis and control.

[0086] The boiling zone stability identification module uses a standardized thermal state dataset to perform fusion calculations on coolant superheat, local temperature difference, and induced point temperature shift. It identifies in real time whether the boiling zone is in a nucleate stable state and maintains a balanced boiling distribution by adjusting the liquid supply rhythm of local channels, thus preventing heat transfer deterioration caused by film boiling.

[0087] The PCM melting state sensing module dynamically monitors the melting coverage of the composite phase change material in the PCM honeycomb panel 8 based on the thermal state dataset, identifies the changing trend of the phase change buffering capacity, and controls the start-stop strategy of the auxiliary condenser 10 based on this to ensure continuous buffering heat dissipation under high heat flux density conditions.

[0088] The emergency condensation response module integrates the boiling zone stability assessment and PCM melting state analysis results to construct a dual-loop condensation control logic that links the main circulation pump 9 and the bidirectional pump 2. In steady state, it maintains normal liquid supply and heat exchange, and in the event of sudden thermal imbalance, it provides rapid emergency heat dissipation by adjusting the pump speed and switching the condensation path, ensuring the safe and stable operation of the battery under extreme conditions.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0090] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart control method for battery immersion cooling with dual phase change coupling, characterized in that: include: S1, collect temperature data and operating characteristic data during battery operation, and preprocess the collected temperature data and operating characteristic data to construct a standardized thermal state dataset; S2, based on a standardized thermal state dataset, performs a stability assessment of the boiling state and dynamically adjusts the local channel liquid supply rhythm based on the stability assessment results. S3, based on the standardized thermal state dataset, perform melt state analysis on the melt coverage of the composite phase change material in the PCM honeycomb panel (8), and drive the change of the start-stop strategy of the auxiliary condenser (10) based on the melt state analysis results; S4, combining the stability assessment results and the molten state analysis results, construct a main and auxiliary dual-loop control structure, and adjust the operating intensity of the main circulation pump (9) and the bidirectional pump (2) in real time.

2. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for collecting temperature data and operational characteristic data during battery operation are as follows: Collect temperature data during battery operation. The temperature data includes: real-time temperature of coolant, saturated boiling point temperature of coolant under current pressure, local temperature difference of coolant in boiling zone, and induced point temperature of PCM honeycomb panel (8) surface. At the same time, calculate and record the average temperature of coolant in the current flow area and the highest temperature value among all measuring points. Collect operational characteristic data, including: the flow rate of the reflux coolant in the condensation circuit, the current total flow rate of the coolant circulation, and the temperature of each thermocouple measuring point in the thermocouple array sensor (6). Record the total number of thermocouple measuring points and the number of melting measuring points whose real-time temperature is equal to or greater than the solid-liquid transition temperature of the material.

3. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for preprocessing the collected temperature data and operational characteristic data to construct a standardized thermal state dataset are as follows: The temperature data is subjected to mean filtering and differential smoothing to suppress noise and spike interference. At the same time, outlier removal and wavelet denoising are performed to obtain stable local temperature difference and induced point temperature values. Boundary correction is used to ensure the accuracy of average temperature and maximum temperature values. The flow signal is filtered and the trend is reconstructed on the operating characteristic data to stabilize the reflux and overall circulation flow. At the same time, the temperature distribution of the thermocouple array sensor (6) is reconstructed by spatial interpolation method, and the number of melting measurement points is accurately obtained through statistical correction. After preprocessing the temperature data and operational characteristic data, a unified normalization and dimensionless transformation is performed to construct a standardized thermal state dataset.

4. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for stability assessment of the boiling state based on the standardized thermal state dataset are as follows: The instantaneous superheat of the coolant is obtained by subtracting the saturation boiling point temperature of the coolant at the current pressure from the real-time temperature value of the coolant. Take the absolute value of the instantaneous superheat of the coolant, add one, and then take the logarithm to obtain the instantaneous temperature fluctuation value. Add one to the local temperature difference value and take the logarithm to obtain the local thermal imbalance value; The absolute value of the difference between the induction point temperature value and the average coolant temperature value is added to one and then the logarithm is taken to obtain the induction deviation value. Multiplying the instantaneous temperature fluctuation value, the local thermal imbalance value, and the induced deviation value yields the boiling stability identification value.

5. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for dynamically adjusting the local channel fluid supply rhythm based on the stability assessment results are as follows: Real-time comparison of the boiling stability identification value and boiling stability threshold of the current flow channel sub-section: When the boiling stability identification value is less than or equal to the boiling stability threshold, the main circulation pump (9) is controlled to maintain the current speed operation state, keep the liquid flow intensity of the main channel unchanged, and at the same time, by controlling the intermittent liquid flow rhythm of the micro-groove array, the distribution balance of the nucleation boiling excitation point is maintained, the boiling stability identification value of the current sub-section is refreshed in real time, and the pump speed adjustment gear is locked. When the boiling stability identification value is greater than the boiling stability threshold, the speed of the main circulation pump (9) is immediately reduced, and the bidirectional pump (2) is simultaneously triggered to reverse low-speed pre-charge, so that the subcooled coolant is injected from the subcooled coolant storage tank (1) into the corresponding box (4); at the same time, the auxiliary condenser (10) and the main condenser (11) are activated to switch the path, and the liquid flow excitation of the original circulation path micro-groove induced structure is suspended, and the liquid is switched to the liquid supply of the flow channel inside the top channel of the PCM honeycomb panel (8).

6. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for analyzing the molten state of the composite phase change material in the PCM honeycomb panel (8) based on the standardized thermal state dataset are as follows: The temperature difference enhancement ratio is obtained by adding the temperature offset factor to the difference between the highest temperature value and the average temperature value of the coolant at all measuring points, and using the highest temperature value as the denominator. Divide the number of melting measurement points by the total number of thermocouple measurement points, add one and take the logarithm to obtain the melting response percentage. Multiplying the temperature difference enhancement ratio by the melting response ratio yields the PCM melt propagation identification value.

7. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for changing the start-up and shutdown strategy of the auxiliary condenser (10) based on the melt state analysis results are as follows: Real-time acquisition of the current PCM melt propagation identification value, and implementation of measures based on the trend of PCM melt propagation identification value changes and the current structural state: When the PCM melt expansion identification value shows an upward trend compared with the PCM melt expansion identification value of the previous measuring point, immediately increase the liquid supply rate of the bidirectional pump (2), switch the coolant inlet path to the bottom channel of the PCM honeycomb plate (8), and open the liquid path of the auxiliary condenser (10) to activate the heat exchange path on the evaporation side. Adjust the speed of the main circulation pump (9) to the lowest value to maintain the coolant flowing through the unmelted area at a low speed. When the PCM melt expansion identification value does not change compared with the PCM melt expansion identification value of the previous measuring point, the temperature in the main channel of the coolant remains unchanged, the current liquid supply state of the bidirectional pump (2) and the power of the auxiliary condenser (10) remain unchanged, and the micro-groove flow channel at the bottom of the PCM honeycomb plate (8) is opened to form a local uniform cold liquid disturbance. When the PCM melt expansion identification value shows a downward trend relative to the PCM melt expansion identification value of the previous measuring point, the temperature sensor (5) reports a decrease in coolant temperature, stops the bidirectional pump (2) from injecting liquid, reduces the operating load of the auxiliary condenser (10), restores the main circulation pump (9) to the default speed, and resets the liquid flow path to the default channel distribution.

8. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: Based on the fusion stability assessment results and fusion state analysis results, the specific steps for constructing the main and auxiliary dual-loop control structure are as follows: Subtract the PCM melt expansion identification value from one and then multiply it by the boiling stability identification value to obtain the boil-melt comprehensive offset value; The absolute value of the difference between the induction point temperature value and the average coolant temperature value is added to one and then the logarithm is taken to obtain the induction deviation value. Divide the reflux coolant flow rate in the condensation circuit by the total coolant circulation flow rate and add one to obtain the condensation reflux compensation value. The combined pump control adjustment value is obtained by dividing the boiling-melting comprehensive offset value by the induced deviation value and then multiplying it by the condensation reflux compensation value.

9. The intelligent control method for battery immersion cooling with dual phase change coupling according to claim 1, characterized in that: The specific steps for real-time adjustment of the operating intensity of the main circulation pump (9) and the bidirectional pump (2) are as follows: Real-time comparison of the current joint pump control adjustment value with the pump control adjustment threshold, which includes a first pump control threshold and a second pump control threshold: When the combined pump control adjustment value is less than or equal to the second pump control threshold, the main circulation pump (9) is set to maintain the current speed and the main channel liquid supply is locked, the liquid injection passage of the bidirectional pump (2) is closed, the existing working conditions of the main condenser (11) and the auxiliary condenser (10) are maintained and the normal liquid flow of the microchannel is maintained; When the combined pump control adjustment value is greater than the second pump control threshold and less than or equal to the first pump control threshold, the speed of the main circulation pump (9) is reduced and the bidirectional pump (2) is turned on to inject subcooled liquid at low speed. The return liquid path is switched to the lower channel of the box (4). At the same time, the heat exchange load of the main condenser (11) is increased and the auxiliary condenser (10) is kept in operation. When the combined pump control adjustment value is greater than the first pump control threshold, the output of the main circulation pump (9) is stopped, the injection speed of the bidirectional pump (2) is increased, the auxiliary condenser (10) is started to run at full load, and the heat exchange capacity of the main condenser (11) is increased to the maximum operation, so that the coolant maintains the maximum flow rate and heat exchange area utilization rate. The local excitation of the V-shaped micro groove is suspended, and the liquid level (12) is adjusted to the height of the emergency channel to maintain the directional flow of coolant in the lower channel of the PCM honeycomb panel (8).

10. A dual-phase-change coupled intelligent control system for battery immersion cooling, employing the dual-phase-change coupled intelligent control method for battery immersion cooling as described in any one of claims 1-9, characterized in that: include: The multi-source data acquisition module is used to collect temperature data and operating characteristic data during battery operation, and to preprocess the collected temperature data and operating characteristic data to construct a standardized thermal state dataset. The boiling zone stability identification module is used to evaluate the stability of the boiling state based on a standardized thermal state dataset, and to dynamically adjust the local channel liquid supply rhythm based on the stability evaluation results. The PCM melt state sensing module is used to analyze the melt state of the composite phase change material in the PCM honeycomb panel (8) based on the standardized thermal state dataset, and drive the start-stop strategy change of the auxiliary condenser (10) based on the melt state analysis results. An emergency condensation response module is used to integrate stability assessment results and melting state analysis results to construct a main and auxiliary dual-loop control structure and adjust the operating intensity of the main circulation pump (9) and the bidirectional pump (2) in real time.

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